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what is krea-2 fr?

konakona666/krea-2 — explained in plain English

Analysis updated 2026-05-18

22PythonAudience · developerComplexity · 4/5LicenseSetup · hard

tl;dr

A fork of the Krea 2 image generation model built for quickly training custom character LoRAs from a folder of reference images.

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what do people make with this?

VIBE 1

Train a custom character LoRA for Krea 2 from a folder of reference images of a person or character.

VIBE 2

Bypass automatic captioning by supplying your own CSV or text file captions for fully offline training.

VIBE 3

Convert a trained character LoRA into a format compatible with ComfyUI workflows.

VIBE 4

Experiment with custom differentiable reward objectives for character training using the included scripts.

what's the stack?

PythonPyTorchLoRAComfyUI

how it stacks up fr

konakona666/krea-2agno-agi/agent-platform-railwayalexantaluo0/acot-vla-wm
Stars222222
LanguagePythonPythonPython
Setup difficultyhardmoderatehard
Complexity4/54/55/5
Audiencedeveloperdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · hard time til it works · 1h+

Requires downloading large Krea 2 model checkpoint files and a GPU capable of running LoRA training.

The code is free to use, modify, and distribute under Apache 2.0, but the Krea 2 model weights themselves follow a separate license with their own commercial use terms.

in plain english

This repository is a fork of Krea AI's Krea 2 image generation model, adapted specifically for training custom character models quickly. Krea 2 itself is a model that generates images, and this fork focuses on teaching it to reproduce a specific character or person using a technique called LoRA training, combined with two training methods named SFT and DRaFT-K. To train a character, you install the training dependencies, point the tool at a Krea 2 checkpoint file, and provide a folder of images of the character you want to train, in formats like JPG, PNG, WebP, BMP, or AVIF. You can optionally give the character a trigger word to invoke it later. By default training runs 500 SFT update steps followed by 60 more focused DRaFT steps, and any missing face detection models needed for training are downloaded automatically. There are a few ways to supply the training data. The tool can automatically write captions for your images and generate its own set of training prompts, or you can supply your own captions as a CSV file or as a matching text file next to each image, along with your own list of prompts. This lets you train fully offline without relying on an external captioning service. Once a character model is trained, there is a script included to convert the resulting file into a format that works with ComfyUI, a popular tool for building image generation workflows. The code in this repository is released under the Apache 2.0 license, though the Krea 2 model weights themselves follow a separate Krea 2 Community License with its own terms for commercial use.

prompts (copy fr)

prompt 1
Help me set up the environment and run train_face.py to train a character LoRA from my own image folder.
prompt 2
Show me how to write a captions.csv file so I can train a character without using DeepInfra.
prompt 3
Walk me through converting my trained character LoRA into a ComfyUI compatible safetensors file.
prompt 4
Explain the difference between the SFT and DRaFT-K training stages used in this repository.

Frequently asked questions

what is krea-2 fr?

A fork of the Krea 2 image generation model built for quickly training custom character LoRAs from a folder of reference images.

What language is krea-2 written in?

Mainly Python. The stack also includes Python, PyTorch, LoRA.

What license does krea-2 use?

The code is free to use, modify, and distribute under Apache 2.0, but the Krea 2 model weights themselves follow a separate license with their own commercial use terms.

How hard is krea-2 to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is krea-2 for?

Mainly developer.

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